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import torch
import torch.nn as nn
import numpy as np
import random
from torch.distributions import Normal
from torch.amp import autocast
from torch.cuda.amp import GradScaler
#device selection
if torch.cuda.is_available():
device = torch.device("cuda")
print("Using CUDA (NVIDIA GPU)")
else:
device = torch.device("cpu")
print("Using CPU")
def set_global_seed(seed: int):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
SEED = 42 #please try run with different seeds to get desired results, we tried with 42, 1,10,20,50.
set_global_seed(SEED)
class MLP(nn.Module):
def __init__(self, input_dim, hidden_dims, output_dim):
super().__init__()
layers = []
last_dim = input_dim
for h in hidden_dims:
layers += [nn.Linear(last_dim, h), nn.ReLU()]
last_dim = h
layers.append(nn.Linear(last_dim, output_dim))
self.net = nn.Sequential(*layers)
def forward(self, x):
return self.net(x)
class Actor(nn.Module):
def __init__(self, obs_dim, mean_field_dim, act_dim, hidden=(64, 64)):
super().__init__()
input_dim = obs_dim + mean_field_dim
self.net = MLP(input_dim, hidden, act_dim)
self.log_std = nn.Parameter(torch.zeros(act_dim))
def forward(self, local_obs, mean_field):
x = torch.cat([local_obs, mean_field], dim=-1)
mean = self.net(x)
LOG_STD_MIN = -5
LOG_STD_MAX = 2
clamped_log_std = torch.clamp(self.log_std, LOG_STD_MIN, LOG_STD_MAX)
std = torch.exp(clamped_log_std)
return Normal(mean, std)
class Critic(nn.Module):
def __init__(self, obs_dim, mean_field_dim, hidden=(128, 128)):
super().__init__()
input_dim = obs_dim + mean_field_dim
self.net = MLP(input_dim, hidden, 1)
def forward(self, local_obs, mean_field):
x = torch.cat([local_obs, mean_field], dim=-1)
return self.net(x).squeeze(-1)
class MFAC:
def __init__(
self,
n_agents,
local_dim,
act_dim,
lr=3e-4,
gamma=0.99,
lam=0.95,
clip_eps=0.2,
k_epochs=10,
batch_size=1024,
entropy_coeff=0.01,
episode_len=96
):
self.n_agents = n_agents
self.local_dim = local_dim
self.mean_field_dim = local_dim
self.act_dim = act_dim
self.gamma = gamma
self.lam = lam
self.clip_eps = clip_eps
self.k_epochs = k_epochs
self.batch_size = batch_size
self.entropy_coeff = entropy_coeff
self.episode_len = episode_len
self.actor = Actor(self.local_dim, self.mean_field_dim, self.act_dim).to(device)
self.critic = Critic(self.local_dim, self.mean_field_dim).to(device)
self.opt_a = torch.optim.Adam(self.actor.parameters(), lr=lr)
self.opt_c = torch.optim.Adam(self.critic.parameters(), lr=lr)
self.use_cuda_amp = (device.type == 'cuda')
self.scaler = GradScaler(enabled=self.use_cuda_amp)
print(f"MFAC CUDA AMP Enabled: {self.use_cuda_amp}")
self.init_buffer()
def init_buffer(self):
self.ls_buf = np.zeros((self.episode_len, self.n_agents, self.local_dim), dtype=np.float32)
self.ac_buf = np.zeros((self.episode_len, self.n_agents, self.act_dim), dtype=np.float32)
self.lp_buf = np.zeros((self.episode_len, self.n_agents), dtype=np.float32)
self.rw_buf = np.zeros((self.episode_len, self.n_agents), dtype=np.float32)
self.done_buf = np.zeros((self.episode_len, self.n_agents), dtype=np.float32)
self.next_ls_buf = np.zeros((self.episode_len, self.n_agents, self.local_dim), dtype=np.float32)
self.step_idx = 0
def clear_buffer(self):
pass
def _get_mean_field(self, obs_batch):
if self.n_agents <= 1:
return torch.zeros(*obs_batch.shape[:-1], self.mean_field_dim, device=obs_batch.device)
total_obs = torch.sum(obs_batch, dim=-2, keepdim=True)
mean_field = (total_obs - obs_batch) / (self.n_agents - 1)
return mean_field
@torch.no_grad()
def select_action(self, local_obs, evaluate=False):
obs_tensor = torch.from_numpy(local_obs).float().to(device)
with autocast(device_type=device.type, dtype=torch.float16, enabled=self.use_cuda_amp):
mean_field = self._get_mean_field(obs_tensor)
dist = self.actor(obs_tensor, mean_field)
if evaluate:
action = dist.mean
else:
action = dist.sample()
log_prob = dist.log_prob(action).sum(-1)
return action.cpu().numpy(), log_prob.cpu().numpy()
def store(self, local_obs, action, logp, reward, done, next_local_obs):
if self.step_idx < self.episode_len:
self.ls_buf[self.step_idx] = local_obs
self.ac_buf[self.step_idx] = action
self.lp_buf[self.step_idx] = logp
self.rw_buf[self.step_idx] = np.array(reward, dtype=np.float32)
self.done_buf[self.step_idx] = np.array(done, dtype=np.float32)
self.next_ls_buf[self.step_idx] = next_local_obs
self.step_idx += 1
def update(self):
T = self.step_idx
if T == 0: return
ls_tensor = torch.from_numpy(self.ls_buf[:T]).float().to(device)
ac_tensor = torch.from_numpy(self.ac_buf[:T]).float().to(device)
lp_tensor = torch.from_numpy(self.lp_buf[:T]).float().to(device)
rw_tensor = torch.from_numpy(self.rw_buf[:T]).float().to(device)
done_tensor = torch.from_numpy(self.done_buf[:T]).float().to(device)
next_ls_tensor = torch.from_numpy(self.next_ls_buf[:T]).float().to(device)
with torch.no_grad():
with autocast(device_type=device.type, dtype=torch.float16, enabled=self.use_cuda_amp):
mf_all = self._get_mean_field(ls_tensor)
vals = self.critic(ls_tensor, mf_all)
next_mf_all = self._get_mean_field(next_ls_tensor)
next_vals = self.critic(next_ls_tensor, next_mf_all)
adv = torch.zeros_like(rw_tensor)
gae = 0
masks = 1.0 - done_tensor
for t in reversed(range(T)):
delta = rw_tensor[t] + self.gamma * next_vals[t] * masks[t] - vals[t]
gae = delta + self.gamma * self.lam * masks[t] * gae
adv[t] = gae
ret = adv + vals
N, D_l = self.n_agents, self.local_dim
ls_flat = ls_tensor.view(T * N, D_l)
mf_flat = mf_all.view(T * N, self.mean_field_dim)
ac_flat = ac_tensor.view(T * N, self.act_dim)
lp_flat = lp_tensor.view(-1)
adv_flat = adv.view(-1)
ret_flat = ret.view(-1)
adv_flat = (adv_flat - adv_flat.mean()) / (adv_flat.std() + 1e-8)
ret_flat = (ret_flat - ret_flat.mean()) / (ret_flat.std() + 1e-8)
dataset = torch.utils.data.TensorDataset(ls_flat, mf_flat, ac_flat, lp_flat, adv_flat, ret_flat)
gen = torch.Generator()
gen.manual_seed(SEED)
loader = torch.utils.data.DataLoader(dataset, batch_size=self.batch_size, shuffle=True, generator=gen)
for _ in range(self.k_epochs):
for b_ls, b_mf, b_ac, b_lp, b_adv, b_ret in loader:
self.opt_a.zero_grad(set_to_none=True)
with autocast(device_type=device.type, dtype=torch.float16, enabled=self.use_cuda_amp):
dist_new = self.actor(b_ls, b_mf)
lp_new = dist_new.log_prob(b_ac).sum(-1)
entropy = dist_new.entropy().sum(-1).mean()
log_ratio = torch.clamp(lp_new - b_lp, -20.0, 20.0)
ratio = torch.exp(log_ratio)
surr1 = ratio * b_adv
surr2 = torch.clamp(ratio, 1 - self.clip_eps, 1 + self.clip_eps) * b_adv
actor_loss = -torch.min(surr1, surr2).mean() - self.entropy_coeff * entropy
self.scaler.scale(actor_loss).backward()
self.scaler.unscale_(self.opt_a)
torch.nn.utils.clip_grad_norm_(self.actor.parameters(), max_norm=0.5)
self.scaler.step(self.opt_a)
self.opt_c.zero_grad(set_to_none=True)
with autocast(device_type=device.type, dtype=torch.float16, enabled=self.use_cuda_amp):
val_pred = self.critic(b_ls, b_mf)
critic_loss = nn.MSELoss()(val_pred, b_ret)
self.scaler.scale(critic_loss).backward()
self.scaler.unscale_(self.opt_c)
torch.nn.utils.clip_grad_norm_(self.critic.parameters(), max_norm=0.5)
self.scaler.step(self.opt_c)
self.scaler.update()
self.step_idx = 0
def save(self, path):
torch.save({
'actor': self.actor.state_dict(),
'critic': self.critic.state_dict()
}, path)
def load(self, path):
data = torch.load(path, map_location=device)
self.actor.load_state_dict(data['actor'])
self.critic.load_state_dict(data['critic']) |